title: Bayesian-based ensemble source apportionment of PM2.5.
abstract: A Bayesian source apportionment (SA) method is developed to provide source impact estimates and associated uncertainties. Bayesian-based ensemble averaging of multiple models provides new source profiles for use in a chemical mass balance (CMB) SA of fine particulate matter (PM2.5). The approach estimates source impacts and their uncertainties by using a short-term application of four individual SA methods: three receptor-based models and one chemical transport model. The method is used to estimate two seasonal distributions of source profiles that are used in SA for a long-term PM2.5 data set. For each day in a long-term PM2.5 data set, 10 source profiles are sampled from these distributions and used in a CMB application, resulting in 10 SA results for each day. This formulation results in a distribution of daily source impacts rather than a single value. The average and standard deviation of the distribution are used as the final estimate of source impact and a measure of uncertainty, respectively. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R(2) = 0.66) and water-soluble potassium (R(2) = 0.63) than source impacts estimated using more traditional methods and more closely agrees with observed total mass. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts and results in fewer days with sources having zero impact. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. This approach can be applied to long-term data sets from speciation network sites of the United States Environmental Protection Agency (U.S. EPA). In addition to providing results that are more consistent with independent observations and known emission sources being present, the distributions of source impacts can be used in epidemiologic analyses to estimate uncertainties associated with the SA results.
authors.affiliation: School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta, Georgia 30332, United States.
authors.forename: Sivaraman
authors.initials: S
authors.lastname: Balachandran
authors.forename: Howard H
authors.initials: HH
authors.lastname: Chang
authors.forename: Jorge E
authors.initials: JE
authors.lastname: Pachon
authors.forename: Heather A
authors.initials: HA
authors.lastname: Holmes
authors.forename: James A
authors.initials: JA
authors.lastname: Mulholland
authors.forename: Armistead G
authors.initials: AG
authors.lastname: Russell
mesh_terms: D001499:Bayes Theorem; D018533:Biomass; D005163:Factor Analysis, Statistical; D005947:Glucose / Q000031:analogs & derivatives / Q000032:analysis; D008956:Models, Chemical; D008962:Models, Theoretical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D011188:Potassium / Q000032:analysis; D012621:Seasons
chemical_list: D052638:Particulate Matter; C014989:1,6-anhydro-beta-glucopyranose; D005947:Glucose; D011188:Potassium